In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...
Abstract: Successive Over-Relaxation Q-learning (SOR-QL) has been proposed recently as an alternative to the widely popular Q-learning algorithm as it is seen to provide better performance where ...
A new technical paper titled “Hardware-Aware Fine-Tuning of Spiking Q-Networks on the SpiNNaker2 Neuromorphic Platform” was published by researchers at TU Dresden, ScaDS.AI and Centre for Tactile ...
Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem ...
College of Mechanical and Electronic Engineering, Shanghai Jianqiao University, Shanghai, China Introduction: To enhance energy management in electric vehicles (EVs), this study proposes an ...
Abstract: Q-learning and double Q-learning are well-known sample-based, off-policy reinforcement learning algorithms. However, Q-learning suffers from overestimation bias, while double Q-learning ...
This guide encapsulates my journey in creating intelligent agents, focusing on a reinforcement learning approach, particularly using the Q-Star method. It offers a practical walkthrough of Microsoft's ...
Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many ...
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